

import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Tuple       )					->				Any:
	__A :			Dict		    =			checkpoints.load_tax_checkpoint(a__       )
	__A :			List[str]		    =			flatten_dict(a__       )
	return flax_params
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Union[str, Any]       )					->				List[Any]:
	__A :			Union[str, Any]		    =			{}

	__A :			Union[str, Any]		    =			{
	    """token_embedder""": """embeddings""",
	    """encoder_norm""": """layernorm""",
	    """kernel""": """weight""",
	    """.out""": """.output""",
	    """scale""": """weight""",
	    """embedders_0.pos_embedding""": """row_embedder.weight""",
	    """embedders_1.pos_embedding""": """column_embedder.weight""",
	}

	__A :			Dict		    =			{
	    """query""": """attention.query""",
	    """key""": """attention.key""",
	    """value""": """attention.value""",
	    """output.dense""": """output""",
	    """encoder_decoder_attention.o""": """encoder_decoder_attention.attention.o""",
	    """pre_self_attention_layer_norm""": """self_attention.layer_norm""",
	    """pre_cross_attention_layer_norm""": """encoder_decoder_attention.layer_norm""",
	    """mlp.""": """mlp.DenseReluDense.""",
	    """pre_mlp_layer_norm""": """mlp.layer_norm""",
	    """self_attention.o""": """self_attention.attention.o""",
	    """decoder.embeddings.embedding""": """decoder.embed_tokens.weight""",
	    """decoder.relpos_bias.rel_embedding""": """decoder.layer.0.self_attention.attention.relative_attention_bias.weight""",
	    """decoder.decoder_norm.weight""": """decoder.final_layer_norm.weight""",
	    """decoder.logits_dense.weight""": """decoder.lm_head.weight""",
	}

	for key in flax_dict.keys():
		if "target" in key:
			# remove the first prefix from the key
			__A :			List[Any]		    =			""".""".join(key[1:]       )

			# rename the key
			for old, new in CONVERSION_MAPPING.items():
				__A :			List[str]		    =			new_key.replace(a__			,a__       )

			if "decoder" in new_key:
				for old, new in DECODER_CONVERSION_MAPPING.items():
					__A :			Optional[Any]		    =			new_key.replace(a__			,a__       )

			if "layers" in new_key and "decoder" not in new_key:
				# use regex to replace the layer number
				__A :			Optional[Any]		    =			re.sub(r"""layers_(\d+)"""			,r"""layer.\1"""			,a__       )
				__A :			Any		    =			new_key.replace("""encoder"""			,"""encoder.encoder"""       )

			elif "layers" in new_key and "decoder" in new_key:
				# use regex to replace the layer number
				__A :			int		    =			re.sub(r"""layers_(\d+)"""			,r"""layer.\1"""			,a__       )

			__A :			Optional[int]		    =			flax_dict[key]

	__A :			Union[str, Any]		    =			{}
	# convert converted_dict into torch format
	for key in converted_dict.keys():
		if ("embed_tokens" not in key) and ("embedder" not in key):
			__A :			Optional[int]		    =			torch.from_numpy(converted_dict[key].T       )
		else:
			__A :			List[str]		    =			torch.from_numpy(converted_dict[key]       )

	return converted_torch_dict
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Any			,a__    :   Union[str, Any]			,a__    :   Optional[Any]=False			,a__    :   Union[str, Any]=False       )					->				Union[str, Any]:
	__A :			Any		    =			get_flax_param(a__       )

	if not use_large:
		__A :			Union[str, Any]		    =			PixaStructVisionConfig()
		__A :			str		    =			PixaStructTextConfig()
	else:
		__A :			List[str]		    =			PixaStructVisionConfig(
		    hidden_size=1536			,d_ff=3968			,num_attention_heads=24			,num_hidden_layers=18       )
		__A :			int		    =			PixaStructTextConfig(hidden_size=1536			,d_ff=3968			,num_heads=24			,num_layers=18       )
	__A :			Union[str, Any]		    =			PixaStructConfig(
	    vision_config=encoder_config.to_dict()			,text_config=decoder_config.to_dict()			,is_vqa=a__       )

	__A :			int		    =			PixaStructForConditionalGeneration(a__       )

	__A :			str		    =			rename_and_convert_flax_params(a__       )
	model.load_state_dict(a__       )

	__A :			Optional[int]		    =			AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer"""       )
	__A :			str		    =			PixaStructImageProcessor()
	__A :			Union[str, Any]		    =			PixaStructProcessor(image_processor=a__			,tokenizer=a__       )

	if use_large:
		__A :			int		    =			4096

	__A :			Dict		    =			True

	# mkdir if needed
	os.makedirs(a__			,exist_ok=a__       )

	model.save_pretrained(a__       )
	processor.save_pretrained(a__       )

	print("""Model saved in {}""".format(a__       )       )


if __name__ == "__main__":
			UpperCAmelCase_							:					Tuple		 =   argparse.ArgumentParser()
			parser.add_argument('''--t5x_checkpoint_path''', default=None, type=str, help='''Path to the original T5x checkpoint.''')
			parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
			parser.add_argument('''--use_large''', action='''store_true''', help='''Use large model.''')
			parser.add_argument('''--is_vqa''', action='''store_true''', help='''Use large model.''')
			UpperCAmelCase_							:					Union[str, Any]		 =   parser.parse_args()

			convert_pixastruct_original_pytorch_checkpoint_to_hf(
			    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
			)







from __future__ import annotations

lowercase    :   Dict						=			tuple[int, int, int]
lowercase    :   List[str]						=			tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
lowercase    :   int						=			'ABCDEFGHIJKLMNOPQRSTUVWXYZ'

# -------------------------- default selection --------------------------
# rotors --------------------------
lowercase    :   Optional[int]						=			'EGZWVONAHDCLFQMSIPJBYUKXTR'
lowercase    :   Optional[Any]						=			'FOBHMDKEXQNRAULPGSJVTYICZW'
lowercase    :   str						=			'ZJXESIUQLHAVRMDOYGTNFWPBKC'
# reflector --------------------------
lowercase    :   Tuple						=			{
    'A': 'N',
    'N': 'A',
    'B': 'O',
    'O': 'B',
    'C': 'P',
    'P': 'C',
    'D': 'Q',
    'Q': 'D',
    'E': 'R',
    'R': 'E',
    'F': 'S',
    'S': 'F',
    'G': 'T',
    'T': 'G',
    'H': 'U',
    'U': 'H',
    'I': 'V',
    'V': 'I',
    'J': 'W',
    'W': 'J',
    'K': 'X',
    'X': 'K',
    'L': 'Y',
    'Y': 'L',
    'M': 'Z',
    'Z': 'M',
}

# -------------------------- extra rotors --------------------------
lowercase    :   Optional[int]						=			'RMDJXFUWGISLHVTCQNKYPBEZOA'
lowercase    :   List[Any]						=			'SGLCPQWZHKXAREONTFBVIYJUDM'
lowercase    :   Dict						=			'HVSICLTYKQUBXDWAJZOMFGPREN'
lowercase    :   List[Any]						=			'RZWQHFMVDBKICJLNTUXAGYPSOE'
lowercase    :   Optional[Any]						=			'LFKIJODBEGAMQPXVUHYSTCZRWN'
lowercase    :   Tuple						=			'KOAEGVDHXPQZMLFTYWJNBRCIUS'




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     RotorPositionT		,    _lowerCamelCase					:     RotorSelectionT		,    _lowerCamelCase					:     str)   ->       tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:






							'''simple docstring'''





							if (unique_rotsel := len(set(_lowerCamelCase))) < 3:
														__UpperCamelCase      :  Tuple       =   F'Please use 3 unique rotors (not {unique_rotsel})'
														raise Exception(_lowerCamelCase)

							# Checks if rotor positions are valid
							__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  str       =   rotpos
							if not 0 < rotorposa <= len(_lowerCamelCase):
														__UpperCamelCase      :  Union[str, Any]       =   F'First rotor position is not within range of 1..26 ({rotorposa}'
														raise ValueError(_lowerCamelCase)
							if not 0 < rotorposa <= len(_lowerCamelCase):
														__UpperCamelCase      :  int       =   F'Second rotor position is not within range of 1..26 ({rotorposa})'
														raise ValueError(_lowerCamelCase)
							if not 0 < rotorposa <= len(_lowerCamelCase):
														__UpperCamelCase      :  List[Any]       =   F'Third rotor position is not within range of 1..26 ({rotorposa})'
														raise ValueError(_lowerCamelCase)

							# Validates string and returns dict
							__UpperCamelCase      :  str       =   _plugboard(_lowerCamelCase)

							return rotpos, rotsel, pbdict




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     str)   ->       dict[str, str]:






							'''simple docstring'''





							if not isinstance(_lowerCamelCase		,    _lowerCamelCase):
														__UpperCamelCase      :  Union[str, Any]       =   F'Plugboard setting isn\'t type string ({type(_lowerCamelCase)})'
														raise TypeError(_lowerCamelCase)
							elif len(_lowerCamelCase) % 2 != 0:
														__UpperCamelCase      :  int       =   F'Odd number of symbols ({len(_lowerCamelCase)})'
														raise Exception(_lowerCamelCase)
							elif pbstring == "":
														return {}

							pbstring.replace(" "		,    "")

							# Checks if all characters are unique
							__UpperCamelCase      :  Optional[int]       =   set()
							for i in pbstring:
														if i not in abc:
																					__UpperCamelCase      :  Tuple       =   F'\'{i}\' not in list of symbols'
																					raise Exception(_lowerCamelCase)
														elif i in tmppbl:
																					__UpperCamelCase      :  Tuple       =   F'Duplicate symbol ({i})'
																					raise Exception(_lowerCamelCase)
														else:
																					tmppbl.add(_lowerCamelCase)
							del tmppbl

							# Created the dictionary
							__UpperCamelCase      :  Union[str, Any]       =   {}
							for j in range(0		,    len(_lowerCamelCase) - 1		,    2):
														__UpperCamelCase      :  Union[str, Any]       =   pbstring[j + 1]
														__UpperCamelCase      :  str       =   pbstring[j]

							return pb




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     str		,    _lowerCamelCase					:     RotorPositionT		,    _lowerCamelCase					:     RotorSelectionT = (rotora, rotora, rotora)		,    _lowerCamelCase					:     str = ""		,    )   ->       str:






							'''simple docstring'''





							__UpperCamelCase      :  List[Any]       =   text.upper()
							__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  Optional[int]       =   _validator(
							    _lowerCamelCase		,    _lowerCamelCase		,    plugb.upper())

							__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  Tuple       =   rotor_position
							__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  List[Any]       =   rotor_selection
							rotorposa -= 1
							rotorposa -= 1
							rotorposa -= 1

							__UpperCamelCase      :  List[str]       =   []

							# encryption/decryption process --------------------------
							for symbol in text:
														if symbol in abc:
																					# 1st plugboard --------------------------
																					if symbol in plugboard:
																												__UpperCamelCase      :  Dict       =   plugboard[symbol]

																					# rotor ra --------------------------
																					__UpperCamelCase      :  Optional[int]       =   abc.index(_lowerCamelCase) + rotorposa
																					__UpperCamelCase      :  List[Any]       =   rotora[index % len(_lowerCamelCase)]

																					# rotor rb --------------------------
																					__UpperCamelCase      :  Dict       =   abc.index(_lowerCamelCase) + rotorposa
																					__UpperCamelCase      :  Any       =   rotora[index % len(_lowerCamelCase)]

																					# rotor rc --------------------------
																					__UpperCamelCase      :  str       =   abc.index(_lowerCamelCase) + rotorposa
																					__UpperCamelCase      :  Union[str, Any]       =   rotora[index % len(_lowerCamelCase)]

																					# reflector --------------------------
																					# this is the reason you don't need another machine to decipher

																					__UpperCamelCase      :  Union[str, Any]       =   reflector[symbol]

																					# 2nd rotors
																					__UpperCamelCase      :  Optional[int]       =   abc[rotora.index(_lowerCamelCase) - rotorposa]
																					__UpperCamelCase      :  Optional[Any]       =   abc[rotora.index(_lowerCamelCase) - rotorposa]
																					__UpperCamelCase      :  str       =   abc[rotora.index(_lowerCamelCase) - rotorposa]

																					# 2nd plugboard
																					if symbol in plugboard:
																												__UpperCamelCase      :  Optional[Any]       =   plugboard[symbol]

																					# moves/resets rotor positions
																					rotorposa += 1
																					if rotorposa >= len(_lowerCamelCase):
																												__UpperCamelCase      :  Any       =   0
																												rotorposa += 1
																					if rotorposa >= len(_lowerCamelCase):
																												__UpperCamelCase      :  Tuple       =   0
																												rotorposa += 1
																					if rotorposa >= len(_lowerCamelCase):
																												__UpperCamelCase      :  List[str]       =   0

        # else:
        #    pass
        #    Error could be also raised
        #    raise ValueError(
        #       'Invalid symbol('+repr(symbol)+')')
														result.append(_lowerCamelCase)

							return "".join(_lowerCamelCase)


if __name__ == "__main__":
		lowercase    :   Optional[Any]						=			'This is my Python script that emulates the Enigma machine from WWII.'
		lowercase    :   int						=			(1, 1, 1)
		lowercase    :   Optional[Any]						=			'pictures'
		lowercase    :   Optional[Any]						=			(rotora, rotora, rotora)
		lowercase    :   Optional[Any]						=			enigma(message, rotor_pos, rotor_sel, pb)

		print('Encrypted message:', en)
		print('Decrypted message:', enigma(en, rotor_pos, rotor_sel, pb))